Encoders in popular types of watermarking system encode a payload into one or more watermark patterns, which are then embedded in the content. Often, the watermark patterns are pseudo-random and the payload is encoded in the circular shift of the patterns. The watermark patterns may be represented in either the temporal or the spatial domain. Alternatively the payload may be encoded directly in a binary watermark pattern, possibly enhanced with error-correcting capabilities, XOR-ed with a pseudo-random sequence, or encoded in some other way.
In such watermark systems a detector accumulates a number of samples, for example audio samples in an audio watermarking system or video frames in a video watermarking system. From these samples, the detector extracts the features in which the watermark was embedded and attempts to match these features with a given detection pattern. For example, in correlation-based watermarking systems the payload embedded in the content can be derived from the presence of correlation peaks and their relative positions. Detection requires that the detector has obtained a large enough accumulation to get high enough correlation peaks, resulting in a reliable detection. We denote the accumulated samples on which the detection is performed by the detection window.
In many watermark systems, the embedder regularly changes the payload, for example to update a time stamp. If there is a payload change within the detection window, the detector may fail to find a good match for the single expected watermark. This may result in detections with lower reliability or even the failure to detect if the peaks are below the detection threshold.
The detector typically does not know in advance that a payload change occurred in the detection window. When it tries to detect on a detection window with a payload change, the parts of the window with different payloads may act on each other as noise, hampering the detection. Or, even worse, the interaction between the different payloads present in the detection window may cause false payloads.
The state of the art does not adequately address this issue. Known methods to mitigate the negative effects of payload changes include                trying detection windows of different sizes, and use running windows to step through the available samples with small step size, which increases the detector complexity, and        designing systems with less frequent payload changes, which limits the granularity of the time stamp in the payload.        
Such methods are limited in their effect and introduce additional complexities or limitations.